
# import Library's


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.graph_objs as go
import plotly.offline as py
import seaborn as sns
import matplotlib.ticker as mtick
plt.style.use('fivethirtyeight')
# https://matplotlib.org/stable/gallery/style_sheets/fivethirtyeight.html
# https://towardsdatascience.com/a-beginners-guide-to-plotting-fivethrityeight-like-visualizations-5b63d3f3ddd0
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import  ExtraTreesRegressor
from sklearn.ensemble import  RandomForestRegressor
from sklearn.model_selection import train_test_split
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
import os


# Import dataset


data = pd.read_csv('G://python//Tablue//zomato.csv')
display(data)


# Check data shape


display(data.shape)

(51717, 17)


print (data.dtypes )

url                            object
address                        object
name                           object
online_order                   object
book_table                     object
rate                           object
votes                           int64
phone                          object
location                       object
rest_type                      object
dish_liked                     object
cuisines                       object
approx_cost(for two people)    object
reviews_list                   object
menu_item                      object
listed_in(type)                object
listed_in(city)                object
dtype: object


# Check for Null Values


print (data.isna().sum() )

url                                0
address                            0
name                               0
online_order                       0
book_table                         0
rate                            7775
votes                              0
phone                           1208
location                          21
rest_type                        227
dish_liked                     28078
cuisines                          45
approx_cost(for two people)      346
reviews_list                       0
menu_item                          0
listed_in(type)                    0
listed_in(city)                    0
dtype: int64


#Deleting Unnecessary Columns
#Dropping the column like "phone" and "url" then create data set "df"


df=data.drop(['url','phone'],axis=1)


# Check for Duplicate Records


df.duplicated().sum()

43


# Display duplicate Records


df[df.duplicated()]


# Drop Duplicate Records


df.drop_duplicates(inplace=True)


display(df.duplicated().sum())

0


# Display Data Frame


display(df)


# display(df)


df.dropna(how='any',inplace=True)


display (df.isnull().sum())

address                        0
name                           0
online_order                   0
book_table                     0
rate                           0
votes                          0
location                       0
rest_type                      0
dish_liked                     0
cuisines                       0
approx_cost(for two people)    0
reviews_list                   0
menu_item                      0
listed_in(type)                0
listed_in(city)                0
dtype: int64


# Display Data Frame


display(df)


# Display Column Names


display(df.columns)

Index(['address', 'name', 'online_order', 'book_table', 'rate', 'votes',
       'location', 'rest_type', 'dish_liked', 'cuisines',
       'approx_cost(for two people)', 'reviews_list', 'menu_item',
       'listed_in(type)', 'listed_in(city)'],
      dtype='object')


# display(df.columns)


df = df.rename(columns={'approx_cost(for two people)':'cost','listed_in(type)':'type', 'listed_in(city)':'city'})
display (df.columns)

Index(['address', 'name', 'online_order', 'book_table', 'rate', 'votes',
       'location', 'rest_type', 'dish_liked', 'cuisines', 'cost',
       'reviews_list', 'menu_item', 'type', 'city'],
      dtype='object')


# Display Data Frame


display (df)


# Get Unique Values from Cost column


display (df['cost']. unique())

array(['800', '300', '600', '700', '550', '500', '450', '650', '400',
       '750', '200', '850', '1,200', '150', '350', '250', '1,500',
       '1,300', '1,000', '100', '900', '1,100', '1,600', '950', '230',
       '1,700', '1,400', '1,350', '2,200', '2,000', '1,800', '1,900',
       '180', '330', '2,500', '2,100', '3,000', '2,800', '3,400', '40',
       '1,250', '3,500', '4,000', '2,400', '1,450', '3,200', '6,000',
       '1,050', '4,100', '2,300', '120', '2,600', '5,000', '3,700',
       '1,650', '2,700', '4,500'], dtype=object)


# Remove the comma separator in the number. 
# Change the data type from string to float
### #Using lambda function to replace ',' with nothing in the cost


df['cost'] = df['cost'].apply(lambda x: x.replace(',','')) 
df['cost'] = df['cost'].astype(float)


# Display Unique Values from Cost column


display (df['cost']. unique())

array([ 800.,  300.,  600.,  700.,  550.,  500.,  450.,  650.,  400.,
        750.,  200.,  850., 1200.,  150.,  350.,  250., 1500., 1300.,
       1000.,  100.,  900., 1100., 1600.,  950.,  230., 1700., 1400.,
       1350., 2200., 2000., 1800., 1900.,  180.,  330., 2500., 2100.,
       3000., 2800., 3400.,   40., 1250., 3500., 4000., 2400., 1450.,
       3200., 6000., 1050., 4100., 2300.,  120., 2600., 5000., 3700.,
       1650., 2700., 4500.])


# Display Data Type of all columns


display (df.dtypes)

address          object
name             object
online_order     object
book_table       object
rate             object
votes             int64
location         object
rest_type        object
dish_liked       object
cuisines         object
cost            float64
reviews_list     object
menu_item        object
type             object
city             object
dtype: object


# Get Unique Values from Rate column


df['rate'].unique()

array(['4.1/5', '3.8/5', '3.7/5', '4.6/5', '4.0/5', '4.2/5', '3.9/5',
       '3.0/5', '3.6/5', '2.8/5', '4.4/5', '3.1/5', '4.3/5', '2.6/5',
       '3.3/5', '3.5/5', '3.8 /5', '3.2/5', '4.5/5', '2.5/5', '2.9/5',
       '3.4/5', '2.7/5', '4.7/5', 'NEW', '2.4/5', '2.2/5', '2.3/5',
       '4.8/5', '3.9 /5', '4.2 /5', '4.0 /5', '4.1 /5', '2.9 /5',
       '2.7 /5', '2.5 /5', '2.6 /5', '4.5 /5', '4.3 /5', '3.7 /5',
       '4.4 /5', '4.9/5', '2.1/5', '2.0/5', '1.8/5', '3.4 /5', '3.6 /5',
       '3.3 /5', '4.6 /5', '4.9 /5', '3.2 /5', '3.0 /5', '2.8 /5',
       '3.5 /5', '3.1 /5', '4.8 /5', '2.3 /5', '4.7 /5', '2.4 /5',
       '2.1 /5', '2.2 /5', '2.0 /5', '1.8 /5'], dtype=object)


# Number of records related to each unique value


display(df['rate'].value_counts().head(60))

3.9/5     1691
3.9 /5    1555
4.0/5     1475
4.0 /5    1427
4.1/5     1417
4.1 /5    1410
3.8/5     1332
3.8 /5    1151
4.2 /5    1122
4.2/5     1002
4.3 /5     909
3.7/5      875
3.7 /5     771
4.3/5      771
4.4 /5     623
4.4/5      519
3.6/5      447
3.6 /5     431
4.5 /5     404
3.5/5      260
4.5/5      244
3.5 /5     222
3.0/5      185
3.4 /5     185
2.9/5      175
4.6 /5     175
3.4/5      166
3.1/5      163
2.8 /5     163
2.8/5      158
NEW        147
3.1 /5     144
2.9 /5     138
3.2/5      137
3.3/5      127
4.6/5      125
3.2 /5     113
3.0 /5     106
2.7/5      105
3.3 /5      92
4.7 /5      86
2.7 /5      85
4.7/5       81
2.6/5       77
2.6 /5      61
4.8 /5      43
2.5 /5      32
2.4/5       30
4.9 /5      30
4.9/5       25
2.5/5       25
2.3/5       25
2.4 /5      24
4.8/5       23
2.3 /5      20
2.2/5       19
2.1 /5      13
2.1/5       11
2.2 /5       7
2.0 /5       7
Name: rate, dtype: int64


# Remove the records related to “New”
# There is one Unique value "NEW", get rid of all records (147 records) with value “New” in the rate column


df = df.loc[df.rate !='NEW'] 
display(df)


# Display Unique Values (Rate Column)


display (df['rate']. unique())

array(['4.1/5', '3.8/5', '3.7/5', '4.6/5', '4.0/5', '4.2/5', '3.9/5',
       '3.0/5', '3.6/5', '2.8/5', '4.4/5', '3.1/5', '4.3/5', '2.6/5',
       '3.3/5', '3.5/5', '3.8 /5', '3.2/5', '4.5/5', '2.5/5', '2.9/5',
       '3.4/5', '2.7/5', '4.7/5', '2.4/5', '2.2/5', '2.3/5', '4.8/5',
       '3.9 /5', '4.2 /5', '4.0 /5', '4.1 /5', '2.9 /5', '2.7 /5',
       '2.5 /5', '2.6 /5', '4.5 /5', '4.3 /5', '3.7 /5', '4.4 /5',
       '4.9/5', '2.1/5', '2.0/5', '1.8/5', '3.4 /5', '3.6 /5', '3.3 /5',
       '4.6 /5', '4.9 /5', '3.2 /5', '3.0 /5', '2.8 /5', '3.5 /5',
       '3.1 /5', '4.8 /5', '2.3 /5', '4.7 /5', '2.4 /5', '2.1 /5',
       '2.2 /5', '2.0 /5', '1.8 /5'], dtype=object)


# display (df['rate']. unique())


df['rate'] = df['rate'].apply(lambda x: x.replace('/5',''))
display (df['rate'].unique())

array(['4.1', '3.8', '3.7', '4.6', '4.0', '4.2', '3.9', '3.0', '3.6',
       '2.8', '4.4', '3.1', '4.3', '2.6', '3.3', '3.5', '3.8 ', '3.2',
       '4.5', '2.5', '2.9', '3.4', '2.7', '4.7', '2.4', '2.2', '2.3',
       '4.8', '3.9 ', '4.2 ', '4.0 ', '4.1 ', '2.9 ', '2.7 ', '2.5 ',
       '2.6 ', '4.5 ', '4.3 ', '3.7 ', '4.4 ', '4.9', '2.1', '2.0', '1.8',
       '3.4 ', '3.6 ', '3.3 ', '4.6 ', '4.9 ', '3.2 ', '3.0 ', '2.8 ',
       '3.5 ', '3.1 ', '4.8 ', '2.3 ', '4.7 ', '2.4 ', '2.1 ', '2.2 ',
       '2.0 ', '1.8 '], dtype=object)


# Visualizations
# Famous Restaurants


# Restaurants with number of outlets


df['name'].value_counts()

Onesta                                    85
Empire Restaurant                         67
KFC                                       59
McDonald's                                57
Pizza Hut                                 56
                                          ..
Le Jardin By Bhagini                       1
Incanto - The Zuri                         1
Salt - Indian Restaurant - Bar & Grill     1
Dilli Dil Se..                             1
Mothi Restaurant                           1
Name: name, Length: 3157, dtype: int64


# df['name'].value_counts()


df['name'].value_counts().head(20)

Onesta                    85
Empire Restaurant         67
KFC                       59
McDonald's                57
Pizza Hut                 56
Domino's Pizza            55
Faasos                    54
Smally's Resto Cafe       54
Corner House Ice Cream    52
Burger King               51
Polar Bear                48
Mani's Dum Biryani        47
Smoor                     47
Pizza Stop                46
Subway                    45
Chung Wah                 45
Mainland China            45
Lakeview Milkbar          44
Hammered                  44
eat.fit                   44
Name: name, dtype: int64


#Create a data frame with top 20 restaurants


chains=df['name'].value_counts()[:20]
display(chains)

Onesta                    85
Empire Restaurant         67
KFC                       59
McDonald's                57
Pizza Hut                 56
Domino's Pizza            55
Faasos                    54
Smally's Resto Cafe       54
Corner House Ice Cream    52
Burger King               51
Polar Bear                48
Mani's Dum Biryani        47
Smoor                     47
Pizza Stop                46
Subway                    45
Chung Wah                 45
Mainland China            45
Lakeview Milkbar          44
Hammered                  44
eat.fit                   44
Name: name, dtype: int64


# Bar chat with Top 20 restaurants with number of outlets


plt.figure(figsize=(20,15))
chains=df['name'].value_counts()[:20]
sns.barplot(x=chains,y=chains.index,palette='deep')
plt.title("Most famous restaurants chains in Bangaluru")
plt.xlabel("Number of outlets")
plt.show()


# Restaurants with Table Booking


# Restaurants with Table Booking


display(df['book_table'].value_counts())

No     17191
Yes     6057
Name: book_table, dtype: int64


# Create a Pie chart


x=df['book_table'].value_counts()
colors = ['#800080', '#0000A0']

trace=go.Pie(labels=x.index,values=x,textinfo="value",
            marker=dict(colors=colors, 
                           line=dict(color='#001000', width=2)))
layout=go.Layout(title="Table booking",width=600,height=600)
fig=go.Figure(data=[trace],layout=layout)
py.iplot(fig, filename='pie_chart_subplots')


# Restaurants with Delivering online or not


# Create a count plot with delivering  online or not


sns.countplot(df['online_order'])
fig = plt.gcf()
fig.set_size_inches(10,10)
plt.title('Whether Restaurants deliver online or Not')
plt.show()


#Rating Distribution


# Ratings distribution - Most of the ratings are within 3.5 and 4.5


plt.figure(figsize=(15,7))
sns.distplot(df['rate'],bins=20)

<AxesSubplot:xlabel='rate', ylabel='Density'>


# Number of Restaurants with Rating Range


# Count of ratings as between "1 and 2", "2 and 3", "3 and 4", and "4 and 5"
# Display Unique Rating


display(df['rate'].unique())

array(['4.1', '3.8', '3.7', '4.6', '4.0', '4.2', '3.9', '3.0', '3.6',
       '2.8', '4.4', '3.1', '4.3', '2.6', '3.3', '3.5', '3.8 ', '3.2',
       '4.5', '2.5', '2.9', '3.4', '2.7', '4.7', '2.4', '2.2', '2.3',
       '4.8', '3.9 ', '4.2 ', '4.0 ', '4.1 ', '2.9 ', '2.7 ', '2.5 ',
       '2.6 ', '4.5 ', '4.3 ', '3.7 ', '4.4 ', '4.9', '2.1', '2.0', '1.8',
       '3.4 ', '3.6 ', '3.3 ', '4.6 ', '4.9 ', '3.2 ', '3.0 ', '2.8 ',
       '3.5 ', '3.1 ', '4.8 ', '2.3 ', '4.7 ', '2.4 ', '2.1 ', '2.2 ',
       '2.0 ', '1.8 '], dtype=object)


# Minimum Rating


display(df['rate'].max())

'4.9 '


# Convert Rating from string to float


df['rate']=df['rate'].astype(float)
display(df['rate'].unique())

array([4.1, 3.8, 3.7, 4.6, 4. , 4.2, 3.9, 3. , 3.6, 2.8, 4.4, 3.1, 4.3,
       2.6, 3.3, 3.5, 3.2, 4.5, 2.5, 2.9, 3.4, 2.7, 4.7, 2.4, 2.2, 2.3,
       4.8, 4.9, 2.1, 2. , 1.8])


# Create Histogram


group= [1,2,3,4,5]
plt.hist (df['rate'],group,histtype = 'bar',rwidth =0.5,color = 'b')
plt.show()


# Number of Restaurants with Rating Between 1 and 2


gr_1to2=((df['rate']>=1) & (df['rate']<2)).sum()
display (gr_1to2)

5


# Number of Restaurants with Rating Between 2 and 3


gr_2to3=((df['rate']>=2) & (df['rate']<3)).sum()
display (gr_2to3)

1179


# Number of Restaurants with Rating Between 3 and 4


gr_3to4=((df['rate']>=3) & (df['rate']<4)).sum()
display (gr_3to4)

10153


# Number of Restaurants with Rating more than 4


gr_4to5=(df['rate']>=4).sum()
display (gr_4to5)

11911


display (gr_4to5)

11911


# # gr_4to5=(df['rate']>=4).sum()


slices=[gr_1to2,gr_2to3,gr_3to4,gr_4to5]


labels=['Rating 1 to 2','Rating 2 to 3','Rating 3 to 4','Rating >4']
colors = ['#ff3333','#c2c2d6','#6699ff']
plt.pie(slices,colors=colors, labels=labels, autopct='%1.0f%%', pctdistance=.5, labeldistance=1.2,shadow=True)
fig = plt.gcf()
plt.title("Percentage of Restaurants according to their ratings")

fig.set_size_inches(10,10)
plt.show()


# Service Type


# Count distinct Value by service Types


display(df['type'].value_counts())

Delivery              10657
Dine-out               8006
Desserts               1364
Cafes                  1066
Drinks & nightlife      879
Buffet                  753
Pubs and bars           523
Name: type, dtype: int64


# Service Type - count Plot


# Here the two main service types are Delivery and Dine-out


sns.countplot(df['type']).set_xticklabels(sns.countplot(df['type']).get_xticklabels(), rotation=90, ha="right")
fig = plt.gcf()
fig.set_size_inches(20,12)
plt.title('Type of Service')
plt.show()


# Distribution of Cost of Food for Two People 

# Display Unique Cost


two_people_cost = df['cost'].unique()
display(np.sort(two_people_cost))

array([  40.,  100.,  120.,  150.,  180.,  200.,  230.,  250.,  300.,
        330.,  350.,  400.,  450.,  500.,  550.,  600.,  650.,  700.,
        750.,  800.,  850.,  900.,  950., 1000., 1050., 1100., 1200.,
       1250., 1300., 1350., 1400., 1450., 1500., 1600., 1650., 1700.,
       1800., 1900., 2000., 2100., 2200., 2300., 2400., 2500., 2600.,
       2700., 2800., 3000., 3200., 3400., 3500., 3700., 4000., 4100.,
       4500., 5000., 6000.])


# Number of Restaurants in each group


df.groupby('cost').size()

cost
40.0         8
100.0      120
120.0        2
150.0      371
180.0       17
200.0      869
230.0       10
250.0      697
300.0     1688
330.0        4
350.0      553
400.0     2478
450.0      719
500.0     2321
550.0      440
600.0     2168
650.0      644
700.0     1202
750.0      651
800.0     1647
850.0      122
900.0      524
950.0       47
1000.0    1218
1050.0       4
1100.0     450
1200.0     903
1250.0       8
1300.0     496
1350.0      18
1400.0     447
1450.0       5
1500.0     841
1600.0     237
1650.0       6
1700.0     229
1800.0     193
1900.0      58
2000.0     298
2100.0      60
2200.0      39
2300.0       1
2400.0      22
2500.0     143
2600.0       4
2700.0       2
2800.0      31
3000.0     159
3200.0       2
3400.0      13
3500.0      25
3700.0       1
4000.0      24
4100.0       4
4500.0       2
5000.0       1
6000.0       2
dtype: int64


# Create a Box Plot


from plotly.offline import iplot
trace0=go.Box(y=df['cost'],name="accepting online orders",
              marker = dict(
        color = 'rgb(113, 10, 100)',
    ))
data=[trace0]
layout=go.Layout(title="Box plot of approximate cost",width=800,height=800,yaxis=dict(title="Price"))
fig=go.Figure(data=data,layout=layout)
py.iplot(fig)


#  Dist plot of cost for the cost


plt.figure(figsize=(8,8))
sns.distplot(df['cost'])
plt.show()


# Most Liked Food


# Extract the favourite food from column – dish_liked
#re=regular expression (use for splitting words). 
# The split is used since dish liked column has multiple values separated by comma, extract each dish, create a list (append each value to list)


import re
df.index=range(df.shape[0])
likes=[]
for i in range(df.shape[0]):
    array_split=re.split(',',df['dish_liked'][i])
    for item in array_split:
        likes.append(item)


# Display the number of dishes and dishes


display ('Number of dishes', len(likes))
display(likes)

'Number of dishes'

126848

['Pasta',
 ' Lunch Buffet',
 ' Masala Papad',
 ' Paneer Lajawab',
 ' Tomato Shorba',
 ' Dum Biryani',
 ' Sweet Corn Soup',
 'Momos',
 ' Lunch Buffet',
 ' Chocolate Nirvana',
 ' Thai Green Curry',
 ' Paneer Tikka',
 ' Dum Biryani',
 ' Chicken Biryani',
 'Churros',
 ' Cannelloni',
 ' Minestrone Soup',
 ' Hot Chocolate',
 ' Pink Sauce Pasta',
 ' Salsa',
 ' Veg Supreme Pizza',
 'Masala Dosa',
 'Panipuri',
 ' Gol Gappe',
 'Onion Rings',
 ' Pasta',
 ' Kadhai Paneer',
 ' Salads',
 ' Salad',
 ' Roti',
 ' Jeera Rice',
 'Farmhouse Pizza',
 ' Chocolate Banana',
 ' Virgin Mojito',
 ' Pasta',
 ' Paneer Tikka',
 ' Lime Soda',
 ' Prawn Pizza',
 'Pizza',
 ' Mocktails',
 ' Coffee',
 ' Nachos',
 ' Salad',
 ' Pasta',
 ' Sandwiches',
 'Waffles',
 ' Pasta',
 ' Coleslaw Sandwich',
 ' Choco Waffle',
 ' Tacos',
 ' Momos',
 ' Cheese Nachos',
 'Waffles',
 ' Pasta',
 ' Crispy Chicken',
 ' Honey Chilli Chicken',
 ' Sandwich',
 ' Coffee',
 ' Crepe',
 'Mocktails',
 ' Peri Fries',
 ' Lasagne',
 ' Pizza',
 ' Chicken Bbq Wings',
 ' Virgin Mojito',
 ' Nachos',
 'Coffee',
 ' Spaghetti',
 ' Pancakes',
 ' Nachos',
 ' Pasta',
 ' Sandwich',
 ' Garlic Bread',
 'Sandwich',
 ' Omelette',
 ' Ice Tea',
 ' Virgin Mojito',
 ' Hot Chocolate',
 ' Pasta Arrabiata',
 ' Hazelnut Cappuccino',
 'Churros',
 ' Cannelloni',
 ' Minestrone Soup',
 ' Hot Chocolate',
 ' Pink Sauce Pasta',
 ' Salsa',
 ' Veg Supreme Pizza',
 'Garlic Bread',
 ' Burgers',
 ' Sandwiches',
 ' Pizza',
 ' Hot Chocolate Fudge',
 ' Waffles',
 ' Potato Wedges',
 'Momos',
 ' Mushroom Bruschettas',
 ' Jalapeno Cheeseballs',
 ' Pasta',
 ' Red Velvet Shake',
 ' Red Velvet Milkshake',
 ' Maggi',
 'Pasta',
 ' Gelato',
 ' Garlic Bread',
 ' Mojito',
 ' Nachos',
 ' Paneer Pizza',
 ' Mocktails',
 'Cheese Maggi',
 ' Peri Peri Fries',
 ' Pasta Arrabiata',
 'Burgers',
 ' Caramel Milkshake',
 ' Iced Coffee',
 ' Iced Tea',
 'Pizza',
 ' Garlic Bread',
 'Pasta',
 ' Iced Tea',
 'Paratha',
 ' Sandwich',
 ' Chilli Idli',
 ' Noodles',
 ' Sandwiches',
 ' Lime Soda',
 'Pizza',
 ' Fries',
 ' Anjeer Ice Cream',
 ' Sandwiches',
 ' Gelato',
 ' Burgers',
 ' Kesar Badam',
 'Burgers',
 ' Lassi',
 ' Chicken Grill',
 ' Naan',
 ' Momos',
 ' Chicken Burger',
 ' Biryani',
 'Paneer Tikka',
 ' Garlic Bread',
 ' Thin Crust Pizza',
 ' Falafel',
 ' Veggie Pizza',
 'Rolls',
 ' Veggie Wrap',
 ' Chocolate Fantasy',
 ' Rice Bowls',
 ' Chicken Rice',
 ' Paneer Makhanwala',
 ' Chicken Tikka Wrap',
 'Raita',
 ' Gulab Jamun',
 ' Paneer Biryani',
 ' Vegetable Biryani',
 ' Gosht Dum Biryani',
 ' Chicken Biryani',
 ' Murgh Biryani',
 'Noodles',
 ' Momos',
 ' Veg Manchow Soup',
 ' Kung Pao Chicken',
 ' Chicken Lollipop',
 ' Chop Suey',
 ' Dragon Chicken',
 'Chicken Tikka Masala',
 ' Mutton Keema Dosa',
 ' Mutton Biryani',
 ' Raan Biryani',
 ' Brain Fry',
 ' Chicken Grill',
 ' Ghee Rice',
 'Dabeli',
 ' Faluda',
 ' Masala Poori',
 ' Sandwiches',
 ' Tokri Chaat',
 ' Pav Bhaji',
 ' Dahipuri',
 'Burgers',
 ' Fries',
 ' Hot Chocolate',
 'Pane Pizza',
 ' Lava Cake',
 ' Pasta',
 ' Stuffed Garlic Bread',
 ' Chocolava Cake',
 ' Tacos',
 'Farmhouse Pizza',
 ' Chocolate Banana',
 ' Virgin Mojito',
 ' Pasta',
 ' Paneer Tikka',
 ' Lime Soda',
 ' Prawn Pizza',
 'Prompt Service',
 'Biryani',
 'Butter Chicken',
 ' Noodles',
 ' Thali',
 ' Biryani',
 'Momos',
 ' Dragon Chicken',
 ' Hakka Noodles',
 ' Chowmein',
 ' Chop Suey',
 ' Chilli Garlic Noodles',
 ' Garlic Rice',
 'Paratha',
 ' Pasta',
 'Hot Chocolate Fudge',
 ' Vanilla Ice Cream',
 ' Fudge Cake',
 ' Chocolate Mousse',
 'Prawn Biryani',
 ' Dragon Chicken',
 ' Chicken Boneless Biryani',
 ' Afghani Biryani',
 ' Mughlai Biryani',
 ' Paneer Tikka Biryani',
 ' Gongura Biryani',
 'Pasta',
 ' Momos',
 ' Rolls',
 ' Chicken Roll',
 ' Chilli Chicken',
 ' Paneer Roll',
 ' Spaghetti',
 'Salads',
 ' Sandwiches',
 ' Salad',
 ' Thai Rice',
 ' Pasta',
 ' Noodles',
 ' Pizza',
 'Sushi',
 ' Noodles',
 ' Wine',
 ' Khau Suey',
 ' Mocktails',
 ' Yakisoba',
 ' Spring Roll',
 'Pasta',
 ' Pepper Pizza',
 ' Paneer Pizza',
 ' Peppy Paneer',
 ' Nachos',
 ' Supreme Pizza',
 ' Manchow Soup',
 'Sea Food',
 ' Crab Meat',
 ' Crab Soup',
 ' Fish Curry',
 ' Chicken Cafreal',
 ' Butter Naan',
 ' Butter Chicken',
 'Salads',
 ' Panneer Butter Masala',
 ' Rabri',
 ' Gajar Ka Halwa',
 ' Manchow Soup',
 ' Aam Panna',
 ' Tomato Basil Soup',
 'Grape Juice',
 ' Chicken Grill',
 ' Chicken Tikka Roll',
 ' Shawarma Roll',
 ' Chicken Shawarama',
 ' Biryani Combo',
 ' Rolls',
 'Beer',
 ' Wedges',
 ' Pizza',
 ' Nachos',
 ' Salads',
 ' Papaya Salad',
 ' Buffalo Wings',
 'Vegetable Biryani',
 ' Ginger Chicken',
 ' Chicken Biryani',
 ' Donne Biryani',
 ' Chicken Kebab',
 ' Egg Bhurji',
 ' Dum Biryani',
 'Rolls',
 ' Momos',
 ' Fries',
 ' Veggie Roll',
 ' Chicken Kebab Roll',
 ' Paneer Roll',
 ' Cheese Roll',
 'Benne Dosa',
 ' Mirchi Bajji',
 ' Filter Coffee',
 ' Poha',
 ' Tea',
 ' Butter Dosa',
 ' Crispy Dosa',
 'Masala Dosa',
 'Berryblast',
 ' Waffles',
 ' Cheesecake',
 ' Salted Caramel',
 'Chicken Boneless Biryani',
 ' Hyderabadi Biryani',
 ' Paneer Biryani',
 ' Tandoori Chicken',
 ' Vegetable Biryani',
 ' Chicken Kebab',
 ' Raita',
 'Biryani',
 ' Chicken Guntur',
 ' Thali',
 ' Buttermilk',
 ' Manchurian',
 'Chilli Chicken',
 'Panipuri',
 ' Chaat',
 ' Noodles',
 ' Dahi Bhalle',
 'Hot Chocolate Fudge',
 'Rooftop Ambience',
 'Coffee',
 ' Tea',
 ' Kesari Bath',
 ' Open Dosa',
 ' Vada',
 ' Idli',
 ' Bread Dosa',
 'Cup Cake',
 ' Chocolate Cake',
 'Masala Dosa',
 ' Tirupathi Pongal',
 ' Filter Coffee',
 ' Vada',
 ' Kharabath',
 ' Kali Dosa',
 ' Rava Idli',
 'Panner Tikka',
 ' Kulfi',
 ' Coffee',
 ' Pav Bhaji',
 ' Carrot Halwa',
 ' Fried Rice',
 ' Set Dosa',
 'Paneer Tikka',
 ' Dal Kichadi',
 ' French Fries',
 'Filter Coffee',
 ' Masala Dosa',
 ' Idli',
 ' Vada',
 'Rolls',
 ' Shawarma',
 ' Butter Chicken',
 ' Mutton Biryani',
 ' Lahori Chicken',
 ' Chicken Roll',
 ' Grape Juice',
 'Rolls',
 ' Paratha',
 ' Chicken Biryani',
 'Paratha',
 ' Mughlai Chicken',
 ' Masala Lemonade',
 ' Butter Chicken',
 ' Rajma',
 ' Sandwich',
 ' Salad',
 'Burgers',
 ' French Fries',
 'Rolls',
 ' Ice Cream Roll',
 ' Paan Ice Cream',
 ' Coffee',
 ' Vanilla Ice Cream',
 ' Chocolate Icecreams',
 ' Mississippi Mudpie',
 'Kadhai Paneer',
 ' Ghee Rice',
 ' Chicken Grill',
 ' Shawarma',
 ' Paratha',
 ' Biryani',
 ' Butter Chicken',
 'Chicken Biryani',
 ' Rolls',
 'Roast Pork',
 ' Tai Pai Chicken',
 ' Beer',
 ' Sweet Corn Chicken Soup',
 ' Masala Peanuts',
 ' Chinese Chop Suey',
 ' Chicken Lollipop',
 'Pizza',
 ' Nachos',
 ' Garlic Maggi',
 ' Burgers',
 ' Cheese Maggi',
 ' Bruschetta Bites',
 ' Sandwich',
 'Noodles',
 ' Laksa Soup',
 ' Coffee',
 ' Veg Fritters',
 ' Vegetarian',
 ' Babycorn Manchurian',
 ' Mango Salad',
 'Lassi',
 'Coffee',
 ' Chaat',
 'Pizza',
 ' Nachos',
 ' Salads',
 ' Peri Peri Fries',
 ' Brownie',
 ' Pasta Alfredo',
 ' Spaghetti',
 'Chicken Grill',
 ' Biryani',
 ' Roti',
 ' Chicken Masala',
 'Chaat',
 ' Pav Bhaji',
 ' Dabeli',
 ' Vada Pav',
 ' Tawa Pulav',
 ' Panipuri',
 ' Malpua',
 'Pizza',
 ' Burgers',
 'Burgers',
 ' Panneer Butter Masala',
 ' Pasta',
 ' Hot Dog',
 ' Fries',
 ' Cheesy Pizza',
 ' Potato Wedges',
 'Sandwiches',
 ' Thick Shakes',
 ' Paan Shake',
 ' Brownie Bomb',
 ' Fries',
 ' Spinach Corn Sandwich',
 ' Paneer Peri Peri',
 'Pizza',
 ' Pasta',
 ' Spinach Salsa',
 ' Garlic Bread',
 ' Lasagne',
 ' Bruschettas',
 ' Chocolate Bomb',
 'Friendly Staff',
 'Biryani',
 'Paratha',
 ' Vada Pav',
 ' Chaat',
 ' Pav Bhaji',
 ' Panipuri',
 ' Masala Poori',
 ' Dhokla',
 'Whole Meat Shawarma',
 ' Fries',
 'Panneer Butter Masala',
 ' Lassi',
 ' Paratha',
 ' Jeera Rice',
 ' Paneer Parantha',
 ' Bhindi Fry',
 ' Veg Kolhapuri',
 'Waffles',
 ' Pasta',
 ' Crispy Chicken',
 ' Honey Chilli Chicken',
 ' Sandwich',
 ' Coffee',
 ' Crepe',
 'Masala Papad',
 ' Paneer Satay',
 ' Vegetable Biryani',
 ' Panneer Butter Masala',
 ' Tomato Soup',
 ' Manchow Soup',
 ' Roti',
 'Shawarma',
 ' Biryani',
 ' Bbq Chicken',
 ' Chicken Grill',
 ' Raita',
 'Jowar Roti',
 ' Buttermilk',
 ' Sabudana Vada',
 ' Thali',
 ' Mirchi Bajji',
 ' Brinjal Curry',
 'Burgers',
 ' Caramel Milkshake',
 ' Iced Coffee',
 ' Iced Tea',
 'Supreme Pizza',
 ' Potato Wedges',
 ' Masala Lemonade',
 ' Tandoori Paneer',
 ' Exotic Garlic Bread',
 'Neer Dosa',
 ' Sea Food',
 ' Ghee Rice',
 ' Marwai Sukka',
 ' Egg Biryani',
 ' Kori Roti',
 ' Thai Fish',
 'White Pasta',
 ' Noodles',
 ' Babycorn Manchurian',
 ' Pizza',
 ' Veg Momos',
 ' Fry Momos',
 ' Tawa Pulav',
 'Masala Dosa',
 ' Sandwiches',
 ' Coffee',
 ' Onion Dosa',
 ' Rava Dosa',
 ' Chaat',
 ' Sandwich',
 'French Fries',
 ' Burgers',
 'Paratha',
 ' Boondi Raita',
 ' Roti',
 ' Kulfi',
 ' Panneer Butter Masala',
 ' Sweet Lassi',
 ' Dal Tadka',
 'Veg Momos',
 ' Brownie Chocolate',
 ' Palak Chaat',
 ' Mutton Biryani',
 ' Dahipuri',
 ' Fish',
 ' Sea Food',
 'Noodles',
 ' Pasta',
 ' Schezwan Momos',
 ' Chicken Momo',
 ' Teriyaki Chicken',
 ' Thukpa',
 ' Fries',
 'Spring Roll',
 ' Manchow Soup',
 ' Babycorn Manchurian',
 ' Mint Cooler',
 ' Roti',
 ' Panneer Butter Masala',
 'Biryani',
 'Pho',
 ' Papaya Salad',
 ' Coffee',
 ' Veg Momos',
 ' Rolls',
 'Waffles',
 ' Pasta',
 ' Coleslaw Sandwich',
 ' Choco Waffle',
 ' Tacos',
 ' Momos',
 ' Cheese Nachos',
 'Pizza',
 ' Pasta',
 ' Salads',
 ' Calamari',
 ' Ravioli',
 ' Pumpkin Soup',
 ' Wine',
 'Nachos',
 ' Cup Cake',
 ' Hot Chocolate Fudge',
 ' Salsa',
 ' Cheesy Garlic Bread',
 ' Waffles',
 ' Tiramisu',
 'Lime Soda',
 ' Manchow Soup',
 ' Biryani',
 ' Lemon Chicken',
 ' Tomato Soup',
 ' Butter Naan',
 ' Chilli Chicken',
 'Murgh Ghee Roast',
 ' Fish Fry',
 ' Ghee Roast Paneer',
 ' Neer Dosa',
 ' Appam',
 ' Sea Food',
 ' Chicken Sukha',
 'Ragi Mudde',
 ' Lemon Chicken',
 ' Chilli Chicken',
 ' Chicken Curry',
 ' Mutton Chops',
 ' Mutton Biryani',
 'Pasta',
 ' Potato Fries',
 ' Potato Wedges',
 ' Hot Chocolate',
 ' Chaat',
 ' Filter Coffee',
 ' Americano',
 'Sweet Lassi',
 ' Gulab Jamun',
 ' Palak Paneer Paratha',
 ' Paneer Parantha',
 'Pizza',
 ' White Sauce Pasta',
 ' Masala Garlic Bread',
 'Burgers',
 ' Fries',
 ' Mojito',
 ' Chicken Burger',
 'Paratha',
 ' Sandwich',
 ' Chilli Idli',
 ' Noodles',
 ' Sandwiches',
 ' Lime Soda',
 'Chicken Biryani',
 ' Mutton Biryani',
 ' Egg Biryani',
 ' Chicken Fry',
 ' Mutton Fry',
 'Chaat',
 ' Brownie',
 ' Onion Rings',
 ' Tomato Soup',
 ' Paneer Manchurian',
 ' Thali',
 ' Faluda',
 'Sandwiches',
 ' Chocolate Sandwich',
 ' Chaat',
 ' Veg Cheese Sandwich',
 ' Chilli Cheese',
 ' Corn Sandwich',
 'Prawn Biryani',
 ' Andhra Meal',
 ' Veg Thali',
 ' Fry Piece Biryani',
 ' Gongura Chicken',
 ' Paratha',
 'Veggie Delight',
 ' Paneer Tikka',
 ' Salad',
 'Faluda',
 ' Brownie',
 ' Sandwich',
 ' Kulfi',
 ' Fruit Salad',
 ' Sweet Lassi',
 ' Chocolate Fudge',
 'Pav Bhaji',
 ' Masala Dosa',
 ' Idli Vada',
 ' Filter Coffee',
 ' Pongal',
 ' Curd Rice',
 ' Kesari Bath',
 'Roti',
 ' Masala Dosa',
 'Onion Rings',
 ' Pasta',
 ' Kadhai Paneer',
 ' Salads',
 ' Salad',
 ' Roti',
 ' Jeera Rice',
 'Masala Papad',
 ' Paneer Satay',
 ' Paneer Hyderabadi',
 ' Butter Naan',
 ' Roti',
 'Stuffed Mushroom',
 ' Chicken Biryani',
 ' Jal-jeera',
 'Filter Coffee',
 ' Tea',
 ' Hot Coffee',
 ' Hatti Samosa',
 'Masala Dosa',
 'Badam Milk',
 ' Potato Twister',
 ' Pav Bhaji',
 ' Panipuri',
 ' Tikki Chaat',
 ' Cheese Cutlet',
 'Pasta',
 ' Iced Tea',
 'Roti',
 ' Butter Kulcha',
 ' Fried Rice',
 ' Tomato Soup',
 ' Curd Rice',
 ' Lunch Buffet',
 ' Paneer Tikka',
 'Chicken Biryani',
 'Chicken Biryani',
 ' Ragi Mudde',
 ' Mutton Chops',
 'Masala Dosa',
 ' Coffee',
 'Cocktails',
 ' Paneer Tikka',
 ' Rasmalai',
 ' Rabri',
 ' Masala Papad',
 ' Vegetable Biryani',
 ' Chicken Curry',
 'Garlic Bread',
 ' Burgers',
 ' Sandwiches',
 ' Pizza',
 ' Hot Chocolate Fudge',
 ' Waffles',
 ' Potato Wedges',
 'Veggie Pizza',
 ' Garlic Bread',
 ' Lava Cake',
 ' Chocolava',
 ' Spicy Paneer',
 'Kharabath',
 ' Crispy Vada',
 ' Filter Coffee',
 ' Idli Vada',
 ' Idli Chutney',
 ' Kesari Bath',
 ' Gulab Jamun',
 'Noodles',
 ' Veg Momos',
 ' Chowmein',
 ' Schezwan Fried Rice',
 ' Clear Soup',
 ' Sweet Corn Soup',
 ' Chicken Wings',
 'Veg Thali',
 ' Matka Kulfi',
 ' Pongal',
 ' Buttermilk',
 ' Vada',
 ' Ragi Mudde',
 ' Curd Rice',
 'Coffee',
 ' Sandwich',
 ' Hot Chocolate',
 ' Ice Tea',
 ' Sandwiches',
 ' Chicken Lasagne',
 ' Pizza',
 'Jowar Roti',
 ' Buttermilk',
 ' Jolada Rotti',
 ' Brinjal Curry',
 ' Coffee',
 ' Pongal',
 ' Vegetable Biryani',
 'Pizza',
 ' Garlic Bread',
 'Sweet Pongal',
 ' Mini Idlis',
 ' Filter Coffee',
 ' Open Masala Dosa',
 ' Pakoda',
 ' Vada',
 ' Raagi Dosa',
 'Paratha',
 ' Coffee',
 ' Halwa',
 ' Lassi',
 'Vegetable Biryani',
 ' Pineapple Rasam',
 ' Cheese Garlic Naan',
 ' Potato Cheese Ball',
 ' Bamboo Chicken Biryani',
 ' Chicken Skewer',
 ' Fish',
 'Malai Kofta',
 ' Sandwich',
 ' Naan',
 'Momos',
 ' Lunch Buffet',
 ' Chocolate Nirvana',
 ' Thai Green Curry',
 ' Paneer Tikka',
 ' Dum Biryani',
 ' Chicken Biryani',
 'Coffee',
 ' Masala Dosa',
 'Sea Food',
 ' Neer Dosa',
 ' Anjal Masala Fry',
 ' Coconut Pudding',
 ' Chicken Biryani',
 ' Roti',
 ' Fish Curry',
 'Tandoori Chicken',
 ' Rolls',
 'Pasta',
 ' Nachos',
 ' Jalapeno Poppers',
 ' Burrito Bowl',
 ' Ferrero Rocher Shake',
 ' Falafal Sandwich',
 ' Banana Waffles',
 'Pizza',
 ' Mocktails',
 ' Coffee',
 ' Nachos',
 ' Salad',
 ' Pasta',
 ' Sandwiches',
 'Aloo Paratha',
 'Rava Dosa',
 'Momos',
 ' Mushroom Bruschettas',
 ' Jalapeno Cheeseballs',
 ' Pasta',
 ' Red Velvet Shake',
 ' Red Velvet Milkshake',
 ' Maggi',
 'Pesto Pizza',
 'Sandwiches',
 ' Noodles',
 ' Sandwich',
 'Lasagne',
 ' Steak',
 ' Masala Quesadilla',
 ' Garlic Bread',
 'Breakfast Buffet',
 ' Kulcha',
 ' Almond Soup',
 ' Lunch Buffet',
 ' Roti',
 ' Mocktails',
 ' Vegetarian',
 'Hara Bhara Kabab',
 ' Crispy Corn',
 ' Patiala Paneer',
 ' Panneer Butter Masala',
 ' Dum Biryani',
 ' Jal-jeera',
 ' Butter Naan',
 'Waffles',
 ' Cup Cake',
 ' Chocolate Cake',
 'Waffles',
 'Momos',
 ' Burgers',
 ' Rolls',
 ' Sandwich',
 ' Hot Chocolate',
 ' Tea',
 ' Naga Thali',
 'Freak Shake',
 ' Fries',
 ' Pasta Alfredo',
 'Butter Chicken',
 ' Chicken Grill',
 ' Tandoori Chicken',
 ' Noodles',
 ' Biryani',
 ' Fried Rice Chicken',
 'Pizza',
 ' Pasta',
 ' Nachos',
 ' Burgers',
 ' Sandwich',
 ' Tea',
 ' Cheese Balls',
 'Masala Chai',
 ' Coffee',
 ' Masala Tea',
 ' Maggi',
 'Pasta',
 ' Gelato',
 ' Garlic Bread',
 ' Mojito',
 ' Nachos',
 ' Paneer Pizza',
 ' Mocktails',
 'Salad',
 ' Mocktails',
 ' Chocolava',
 ' Pesto Pasta',
 ' Momos',
 ' Jasmine Rice',
 ' Tom Yum Soup',
 'Vada',
 ' Masala Dosa',
 ' Idli',
 'Coffee',
 ' Masala Dosa',
 ' Vegetable Biryani',
 'Pasta',
 ' Pizza',
 ' Burgers',
 ' Pancakes',
 ' Garlic Bread',
 'Maggi',
 ' Pasta',
 ' Burgers',
 ' Sandwiches',
 ' Fries',
 'Sea Food',
 ' Neer Dosa',
 'Pizza',
 'Brownie',
 ' Mojito',
 ' Prawn Ghee Roast',
 ' Corn Tikki',
 ' Mushroom Ghee Roast',
 ' Pinacolada',
 ' Biryani',
 'Biryani',
 ' Thick Shakes',
 ' Fries',
 'Vegetarian',
 'Sweet Pongal',
 ' Curd Rice',
 'Pesto Pizza',
 ' Buffalo Wings',
 ' Cocktails',
 ' Beer',
 ' Egg Ghee Roast',
 ' Brownie',
 ' Pepper Chicken',
 'Waffles',
 ' Sandwiches',
 ' Maggi',
 ' Thick Shakes',
 ' Chocolate Sandwich',
 'Honey Chilli Potatoes',
 ' Nachos',
 ' Lemonade',
 ' Hakka Noodles',
 ' Babycorn Manchurian',
 ' Mushroom Manchurian',
 ' Spring Roll',
 'Manchow Soup',
 ' Momos',
 ' Wonton Soup',
 ' Chowmein',
 ' American Chopsuey',
 ' Chop Suey',
 ' Manchurian',
 'Pasta',
 ' Pizza',
 ' Nachos',
 ' Gelato',
 ' Ravioli',
 ' Jalapeno Poppers',
 ' Cocktails',
 'Masala Vada Pav',
 ' Cheese Vada Pav',
 ' Fries',
 'Corn Augratin',
 ' Lasagne',
 ' Fries',
 ' Garlic Bread',
 ' Pili Pili Pizza',
 ' Mexican Rice',
 ' Ravioli',
 'Salads',
 ' Sandwiches',
 ' Pasta',
 ' Mocktails',
 ' Corn Sandwich',
 ' Garden Salad',
 ' Miso Salad',
 'Coffee',
 ' Thali',
 ' Kharabath',
 ' Masala Dosa',
 ' Idli Vada',
 ' Vanilla Ice Cream',
 ' Bisi Bele Bhaath',
 'Veg Jaipuri',
 ' Neer Dosa',
 ' Kulcha',
 ' Pepper Chicken',
 ' Chilli Mushroom',
 ' Murgh Platter',
 ' Paneer Tikka',
 'Pizza',
 ' Pasta Alfredo',
 ' Brownie',
 ' Garlic Bread',
 ' Sandwiches',
 ' Lemon Mojito',
 ' Mocha Fudge',
 'Mocktails',
 ' Tomato Soup',
 ' Pasta',
 ...]


# Index


df.index=range(df.shape[0])
df.index

RangeIndex(start=0, stop=23248, step=1)


# Display top 30 dishes


print("Count of Most liked dishes")
favourite_food = pd.Series(likes).value_counts()
display(favourite_food.head(30))

Count of Most liked dishes

 Pasta              2692
 Pizza              1915
 Cocktails          1880
 Burgers            1736
 Mocktails          1623
 Biryani            1307
 Sandwiches         1287
Burgers             1256
 Coffee             1184
 Nachos             1170
 Fish               1116
 Paratha            1107
 Salads             1055
 Chicken Biryani    1004
Cocktails            891
 Fries               876
 Noodles             854
 Beer                835
 Mutton Biryani      832
 Tea                 819
Coffee               801
 Sandwich            788
 Butter Chicken      782
 Thali               770
Biryani              749
Pizza                747
 Roti                729
 Brownie             726
 Salad               677
 Hot Chocolate       672
dtype: int64


# Create Bar Graph with top 20 dishes 
#We can infer from the analysis that the 5 most liked dishes are Pasta,Pizza,Cocktails,Burgers,and Mocktails


ax = favourite_food.nlargest(n=20, keep='first').plot(kind='bar',figsize=(20,15),title = 'Top 30 Favourite Food counts ')

for i in ax.patches:
    ax.annotate(str(i.get_height()), (i.get_x() * 1.005, i.get_height() * 1.005))


# Restaurant Type and Count 
# Restaurant Type and Count


df['rest_type'].value_counts().head(50)

Casual Dining                    7326
Quick Bites                      5250
Cafe                             2375
Dessert Parlor                   1083
Casual Dining, Bar                985
Delivery                          704
Bar                               358
Takeaway, Delivery                342
Bar, Casual Dining                336
Pub                               324
Fine Dining                       311
Casual Dining, Cafe               304
Lounge                            298
Pub, Casual Dining                224
Food Court                        220
Bakery                            166
Cafe, Casual Dining               159
Dessert Parlor, Cafe              143
Beverage Shop                     137
Beverage Shop, Quick Bites        137
Cafe, Bakery                      128
Casual Dining, Pub                124
Microbrewery, Casual Dining       121
Cafe, Dessert Parlor              111
Sweet Shop                        105
Bakery, Quick Bites                96
Sweet Shop, Quick Bites            88
Pub, Microbrewery                  76
Dessert Parlor, Quick Bites        71
Bakery, Cafe                       69
Bakery, Dessert Parlor             62
Quick Bites, Dessert Parlor        55
Mess                               49
Dessert Parlor, Beverage Shop      49
Casual Dining, Microbrewery        47
Beverage Shop, Dessert Parlor      44
Kiosk                              44
Microbrewery, Pub                  42
Quick Bites, Sweet Shop            41
Pub, Cafe                          40
Cafe, Quick Bites                  40
Fine Dining, Bar                   40
Lounge, Casual Dining              37
Club                               37
Lounge, Bar                        36
Quick Bites, Bakery                35
Food Court, Quick Bites            34
Quick Bites, Beverage Shop         31
Microbrewery                       28
Pub, Bar                           28
Name: rest_type, dtype: int64


# Create Graph with top 20 restaurant type 
#Casual Dining, Quick Bites and Cafe are the 3 most common types of Restaurants


plt.figure(figsize=(15,7))
rest=df['rest_type'].value_counts()[:20]
sns.barplot(rest,rest.index)
plt.title("Restaurant types")
plt.xlabel("count")
plt.show()


# Famous Restaurants


plt.figure(figsize=(15,7))
chains=df['name'].value_counts()[:20]
sns.barplot(x=chains,y=chains.index,palette='Set1')
plt.title("Most famous restaurant chains",size=20,pad=20)
plt.xlabel("Number of outlets",size=15)
plt.show()


# Model Creation
# Display top 5 records


display(df.head())


# Convert the online categorical variables into a numeric format


df.online_order[df.online_order == 'Yes'] = 1 
df.online_order[df.online_order == 'No'] = 0


# Display number of records in each category


df.online_order.value_counts()

1    16378
0     6870
Name: online_order, dtype: int64


# Change the data type to Number, display top 20 records


df.online_order = pd.to_numeric(df.online_order)
display (df.head(20))


# Update Column book_table to 1 and 0 
#change the string categorical into to a categorical int


df.book_table[df.book_table == 'Yes'] = 1 
df.book_table[df.book_table == 'No'] = 0


# Change the data type


df.book_table = pd.to_numeric(df.book_table)


# df.book_table = pd.to_numeric(df.book_table)


display(df.book_table.value_counts())

0    17191
1     6057
Name: book_table, dtype: int64


# Label Encoding of required column
#Label encode the categorical variables to make it easier to build algorithm


from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df.location = le.fit_transform(df.location)
df.rest_type = le.fit_transform(df.rest_type)
df.cuisines = le.fit_transform(df.cuisines)
df.menu_item = le.fit_transform(df.menu_item)


# Get the required columns to create model


my_data=df.iloc[:,[2,3,4,5,6,7,9,10,12]]
my_data.to_csv('Zomato_df.csv')
display (my_data)


# Create independent Variable


x = df.iloc[:,[2,3,5,6,7,9,10,12]]
x.head()


# Create Dependent Variable


y = df['rate']
display(y)

0        4.1
1        4.1
2        3.8
3        3.7
4        3.8
        ... 
23243    3.8
23244    3.9
23245    2.8
23246    2.5
23247    4.3
Name: rate, Length: 23248, dtype: float64


# Train Test Split


x_train, x_test,y_train,y_test=train_test_split(x,y,test_size=.3,random_state=10)


# Create Linear Regression Model


lr_model=LinearRegression()
lr_model.fit(x_train,y_train)

LinearRegression()


# Prediction and display Accuracy


from sklearn.metrics import r2_score
y_pred=lr_model.predict(x_test)
display (r2_score(y_test,y_pred))

0.22818828522967471


# Random Forest Regressor


from sklearn.ensemble import RandomForestRegressor
RF_Model=RandomForestRegressor(n_estimators=650,random_state=245,min_samples_leaf=.0001)
RF_Model.fit(x_train,y_train)
y_predict=RF_Model.predict(x_test)
display (r2_score(y_test,y_predict))

0.8809706960047533


# Preparing Extra Tree Regression


from sklearn.ensemble import  ExtraTreesRegressor
ET_Model=ExtraTreesRegressor(n_estimators = 120)
ET_Model.fit(x_train,y_train)
y_predict=ET_Model.predict(x_test)


# Display Accuracy


from sklearn.metrics import r2_score
display(r2_score(y_test,y_predict))

0.932724849640803


# Generate Pickle File


import pickle 
# Saving model to disk
pickle.dump(ET_Model, open('model.pkl','wb'))
model=pickle.load(open('model.pkl','rb'))

	address	name	online_order	book_table	rate	votes	location	rest_type	dish_liked	cuisines	approx_cost(for two people)	reviews_list	menu_item	listed_in(type)	listed_in(city)
596	572, 30th Main, 3rd Stage, Banashankari, Banga...	My Tea House	Yes	Yes	NEW	0	Banashankari	Casual Dining	NaN	Continental, Asian, North Indian, Tea	500	[('Rated 4.0', "RATED\n So apparently I visit...	['MTH Signature Soup', 'Minestrone Veg', 'Broc...	Dine-out	Banashankari
7599	17, Thubarahalli Village, Hobli, Varthur Main ...	Nawab Di Biryani	Yes	No	NEW	0	Whitefield	Takeaway, Delivery	NaN	Biryani, Mughlai	400	[('Rated 4.0', 'RATED\n Ordered paneer tikka ...	['Paneer Tikka Biryani', 'Veg Lucknowi Biryani...	Delivery	Brookefield
9464	744, 13th Main, 7th Cross, BTM 2nd Stage, BTM,...	Vyanjan	Yes	No	3.9/5	201	BTM	Quick Bites	Poha, Jalebi, Paratha, Bhindi Masala, Kachori,...	North Indian, Street Food	300	[('Rated 3.0', "RATED\n Nice place, it's for ...	['Jeera Rice', 'Khichdi', 'Plain Tawa Roti', '...	Delivery	BTM
9466	607, First Floor, 80 Feet Road, 6th Block, Kor...	Carrots	Yes	Yes	4.1/5	600	Koramangala 6th Block	Casual Dining	Pizza, Pasta, Salads, Masala Chai, Soya Butter...	Italian, North Indian, Healthy Food, Desserts	1,100	[('Rated 5.0', "RATED\n It's the first vegan ...	[]	Delivery	BTM
9467	Building 58, New 3, A Main Road Marenahalli, J...	Eatsomnia	Yes	No	NEW	0	JP Nagar	Delivery	NaN	North Indian, South Indian, Chinese, Continental	500	[('Rated 1.0', "RATED\n The worst food I ate ...	[]	Delivery	BTM
9468	6th Cross Road, Koramangala 6th Block, Bangalore	Rolls & Co	Yes	No	3.9/5	213	Koramangala 6th Block	Quick Bites	Rolls, Chicken Wings, Shawarma, Chicken Tikka ...	Burger, Beverages, Rolls	400	[('Rated 2.0', 'RATED\n Order : Peri Peri Fri...	[]	Delivery	BTM
9469	10, Opposite Forum Mall, Taverekere, Suddagunt...	Fogg	No	Yes	NEW	0	BTM	Quick Bites	NaN	Fast Food, Chinese	200	[('Rated 5.0', 'RATED\n Seafood soup - A Thai...	[]	Delivery	BTM
9470	Koramangala 1st Block, Bangalore	The Paratha Stories	Yes	No	3.3/5	36	Koramangala 1st Block	Delivery	NaN	North Indian	400	[('Rated 3.0', 'RATED\n There is enough scope...	[]	Delivery	BTM
9471	29, 7th Cross Road, Bhavani Nagar, Suddagunte ...	Mycakeshop	Yes	No	NaN	0	BTM	Bakery	NaN	Bakery, Desserts	300	[('Rated 5.0', 'RATED\n I loved cake nice tru...	['Red Velvet Cake', 'Truffle Chocolate Cake', ...	Delivery	BTM
9549	51, 5th Main, 60 Feet Road, BNG Homestay Build...	Bisimillah Biriyani	No	No	3.5/5	11	BTM	Quick Bites	NaN	Biryani, Fast Food	300	[('Rated 4.0', 'RATED\n Very delicious food @...	[]	Delivery	BTM
9638	51, 5th Main, 60 Feet Road, BNG Homestay Build...	Bisimillah Biriyani's & Kebab's	No	No	3.3/5	6	BTM	Quick Bites	NaN	Biryani, Fast Food	300	[]	[]	Delivery	BTM
12287	Q Court, 3rd Floor, Garuda Mall, Magarth Road,...	China South	Yes	No	NaN	0	Brigade Road	Casual Dining	NaN	Chinese, Tibetan	600	[('Rated 4.0', 'RATED\n Been at this place tw...	['Egg Fried Rice', 'Chicken American Chopsuey'...	Delivery	Church Street
12288	24/48, M Road Benson Town Frazer Town, Bangalore	Chination	No	No	NaN	0	Frazer Town	Delivery	NaN	Chinese	600	[]	[]	Delivery	Church Street
12289	Garuda Mall, 3rd Floor, 15 Magrath Road, Craig...	Desi Streat	Yes	No	NaN	0	Brigade Road	Food Court	NaN	South Indian, Street Food	400	[('Rated 1.0', "RATED\n Try asking for combo....	['Sambar Idli', 'Moong Chilla with Paneer Bhur...	Delivery	Church Street
12290	KH Marigowda Road, Near Hopcoms Complex, Shant...	Durga Sagar	Yes	No	3.3/5	8	Shanti Nagar	Quick Bites	NaN	South Indian	150	[('Rated 4.0', 'RATED\n Ordered Anna sambar.....	['4 Pakoda', 'Green Salad', 'Cucumber Salad', ...	Delivery	Church Street
12291	34/2, Meaner Avenue Tank Road, Near Ulsoor Lak...	Happy Fillings	Yes	No	3.4/5	8	Ulsoor	Quick Bites	NaN	Chinese	300	[('Rated 5.0', 'RATED\n A Good take away Chin...	[]	Delivery	Church Street
12292	25, Gover Road, Cox Town, Frazer Town, Bangalore	Marathi Biriyani House	Yes	No	3.3/5	6	Frazer Town	Quick Bites	NaN	South Indian, Biryani	300	[('Rated 4.0', 'RATED\n Happened to just come...	[]	Delivery	Church Street
12294	19, Saunder Road, Opposite Seventh Day Hospita...	Kareem's	Yes	No	3.6/5	32	Frazer Town	Quick Bites	NaN	North Indian, Mughlai	700	[('Rated 4.0', 'RATED\n Seen this restaurant ...	['Kareen Special Murgh Tandoori', 'Gosht Seekh...	Delivery	Church Street
12296	307, Thimmaiah Rd, Govinda Chetty Colony, Shiv...	Aubree	Yes	No	NaN	0	Shivajinagar	Cafe, Bakery	NaN	Cafe, Bakery	400	[('Rated 5.0', "RATED\n Aubree has become a v...	['Strawberry Macarons [Box of 5]', 'Lite Touch...	Delivery	Church Street
12303	1, High Street, Cooke Town, Frazer Town, Banga...	Mudaliar's Kitchen	Yes	No	NaN	0	Frazer Town	Delivery	NaN	South Indian	500	[]	[]	Delivery	Church Street
12314	19/1, Queen's Road Cross, Munniswamy Road, Nea...	Ta'aam	Yes	No	4.1/5	402	Cunningham Road	Casual Dining	Laham Mandi, Mutton Chops, Biryani, Mutton Sou...	North Indian, Mughlai, Arabian, Chinese	750	[('Rated 4.0', "RATED\n For all gluttons out ...	[]	Delivery	Church Street
12322	Alpine Annexed Apartment, Near ICICI Bank, Sha...	Promac Foods	Yes	No	NEW	0	Shanti Nagar	Takeaway, Delivery	NaN	North Indian, South Indian	250	[]	[]	Delivery	Church Street
12347	Plot 62, Nanjappa Layout, Adugodi Main Road, A...	Ascharya Hotel	Yes	No	3.4/5	11	Koramangala 5th Block	Quick Bites	NaN	Mangalorean, South Indian	300	[('Rated 3.0', 'RATED\n I would say that thei...	[]	Delivery	Church Street
13442	Opposite Velankani Gate number 2, Doddathoguru...	Lassi Adda	Yes	No	4.0/5	65	Electronic City	Beverage Shop	Sweet Lassi, Corn Sandwich	Beverages, Sandwich, Fast Food	200	[('Rated 4.0', 'RATED\n My favourite go to pl...	[]	Delivery	Electronic City
13467	65, TS Arcade, Next To Michelin Tyre Centre, C...	Sitaar	Yes	Yes	3.8/5	88	Electronic City	Casual Dining	NaN	North Indian, Chinese	800	[('Rated 5.0', "RATED\n Really good food and ...	[]	Delivery	Electronic City
17655	Opposite Sri Subramanya Swamy Temple, Next to ...	New Taj	Yes	No	3.3/5	110	Ulsoor	Quick Bites	NaN	North Indian, Chinese	300	[('Rated 2.0', "RATED\n It's my nature to try...	[]	Delivery	Indiranagar
20236	746, 7th cross, 13th Main, BTM, Bangalore	Funky Punjab	Yes	No	3.8/5	125	BTM	Quick Bites	Roti, Dal Makhani, Lassi, Panneer Butter Masal...	North Indian, Chinese	400	[('Rated 3.0', "RATED\n Wasn't as great as we...	[]	Delivery	Jayanagar
20836	36, Jaya Bheema Nagar, Layout 1, BTM, Bangalore	La Passion Cafe	Yes	No	NaN	0	BTM	Cafe	NaN	Cafe	500	[]	[]	Delivery	Jayanagar
20851	3,11th Cross, Maruthi Nagar, 1st Stage, BTM, B...	Royal's Biryani	Yes	No	NaN	0	BTM	Quick Bites	NaN	Biryani	300	[('Rated 4.0', 'RATED\n An amazing biryani pl...	[]	Delivery	Jayanagar
20852	14, Tank Shore Road, Kuvempu Nagar, Stage 2, B...	Tandoor Nation	Yes	No	3.0/5	39	BTM	Quick Bites	NaN	North Indian, Chinese	400	[('Rated 3.0', 'RATED\n Calm, cozy and open s...	['Chicken Curry', 'Chicken Pepper Dry', 'Chick...	Delivery	Jayanagar
20853	242, 7th Main, Mico Layout, BTM, Bangalore	Indian Kitchen Eateries-IKE	Yes	No	3.4/5	8	BTM	Quick Bites	NaN	Fast Food	150	[('Rated 5.0', 'RATED\n One of the best snack...	[]	Delivery	Jayanagar
26434	10, Babasabarapalya, Near Panchamukhi Ganesha ...	The Bridge Walk Cafe	No	No	3.8 /5	74	Kengeri	Cafe	NaN	Cafe, Continental	500	[('Rated 3.0', 'RATED\n This cafe opened up f...	[]	Dine-out	Kammanahalli
28139	1086/A, Twin Tulips, 18th Cross, 14th Main Roa...	Wedding Biryani	No	No	NEW	0	HSR	Quick Bites	NaN	Biryani, Kebab	600	[]	[]	Delivery	Koramangala 4th Block
37692	Ashraya Hotel, 149, Infantry Road, Bangalore	The Oriental Spice - Ashraya Hotel	Yes	No	3.9 /5	158	Infantry Road	Casual Dining	Noodles, Lunch Buffet, Fish, Manchurian, Salad...	Chinese, Thai, Indonesian	1,300	[('Rated 4.0', 'RATED\n Had been there for a ...	['Sour Pepper Soup', 'Chicken Cantonese Style'...	Buffet	Lavelle Road
38109	99, 3rd Main Road, Hanumanthnagar, Banashankar...	Sai Super Sandwich	Yes	No	NEW	0	Banashankari	Quick Bites	NaN	Sandwich, Beverages	200	[]	[]	Delivery	Lavelle Road
38585	Level 2, The Collection, UB City, Vithal Mally...	Rasovara	Yes	Yes	4.3 /5	772	Lavelle Road	Casual Dining	Thali, Aamras, Malpua, Dal Bati, Lassi, Plain ...	Rajasthani, North Indian	1,400	[('Rated 4.0', 'RATED\n The problem here is a...	[]	Dine-out	Lavelle Road
41404	168/4, Ward 57, Abbaiah Reddy, Layout Main Roa...	Andhra Express	Yes	No	3.7 /5	19	CV Raman Nagar	Takeaway, Delivery	NaN	Andhra, Biryani, North Indian, Beverages	300	[('Rated 5.0', 'RATED\n Amazing take away / h...	[]	Delivery	Marathahalli
42312	12/1, Swami Vivekananda Road, Jayaraj Nagar, S...	Shahi Family Restaurant	Yes	No	3.2 /5	88	Ulsoor	Quick Bites	NaN	North Indian, Chinese, South Indian	350	[('Rated 3.0', 'RATED\n Tried chicken biriyan...	['Veg Chinese Combo', 'Non Veg Chinese Combo',...	Delivery	MG Road
42343	Money Chambers Double Road, Shanti Nagar, Bang...	Red Onion	Yes	Yes	4.2 /5	570	Shanti Nagar	Casual Dining	Fish, Dumplings, Biryani, Paneer Tikka Masala,...	Chinese, North Indian, Biryani, Kebab	1,200	[('Rated 5.0', 'RATED\n Was wandering around ...	['Paneer Makhani', 'Hyderabadi Biryani', 'Spec...	Delivery	MG Road
43354	36, Vittal Mallya Road, Lavelle Road, Bangalore	Bootlegger	No	No	4.1 /5	1545	Lavelle Road	Pub	Beer, Cocktails, Beef Steak, Beef Chilli, Nach...	Continental, Steak	1,200	[('Rated 2.0', 'RATED\n Bootlegger is undoubt...	[]	Dine-out	MG Road
43369	Chancery Pavilion, 135, Residency Road, Bangalore	Ithaca - The Chancery Pavilion	No	Yes	3.9 /5	312	Residency Road	Fine Dining	Salads, Sunday Brunch, Cocktails, Watermelon J...	North Indian, Italian, European, Continental	2,000	[('Rated 3.0', 'RATED\n The food was good\nTh...	[]	Dine-out	MG Road
46118	1200, 100 Feet Road, HAL 2nd Stage, Indiranaga...	Boozers Bar & Kitchen	No	No	4.3 /5	700	Indiranagar	Bar, Casual Dining	Beer, Cocktails, Chicken Burger, Pasta, Tandoo...	Continental, Burger, Italian, Pizza, North Ind...	NaN	[('Rated 2.0', 'RATED\n A relatively new. I g...	[]	Pubs and bars	Old Airport Road
50904	17, Thubarahalli Village, Hobli, Varthur Main ...	Nawab Di Biryani	Yes	No	NEW	0	Whitefield	Takeaway, Delivery	NaN	Biryani, Mughlai	400	[('Rated 3.0', 'RATED\n Biriyani had ordered ...	[]	Delivery	Whitefield

	url	address	name	online_order	book_table	rate	votes	phone	location	rest_type	dish_liked	cuisines	approx_cost(for two people)	reviews_list	menu_item	listed_in(type)	listed_in(city)
0	https://www.zomato.com/bangalore/jalsa-banasha...	942, 21st Main Road, 2nd Stage, Banashankari, ...	Jalsa	Yes	Yes	4.1/5	775	080 42297555\r\n+91 9743772233	Banashankari	Casual Dining	Pasta, Lunch Buffet, Masala Papad, Paneer Laja...	North Indian, Mughlai, Chinese	800	[('Rated 4.0', 'RATED\n A beautiful place to ...	[]	Buffet	Banashankari
1	https://www.zomato.com/bangalore/spice-elephan...	2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...	Spice Elephant	Yes	No	4.1/5	787	080 41714161	Banashankari	Casual Dining	Momos, Lunch Buffet, Chocolate Nirvana, Thai G...	Chinese, North Indian, Thai	800	[('Rated 4.0', 'RATED\n Had been here for din...	[]	Buffet	Banashankari
2	https://www.zomato.com/SanchurroBangalore?cont...	1112, Next to KIMS Medical College, 17th Cross...	San Churro Cafe	Yes	No	3.8/5	918	+91 9663487993	Banashankari	Cafe, Casual Dining	Churros, Cannelloni, Minestrone Soup, Hot Choc...	Cafe, Mexican, Italian	800	[('Rated 3.0', "RATED\n Ambience is not that ...	[]	Buffet	Banashankari
3	https://www.zomato.com/bangalore/addhuri-udupi...	1st Floor, Annakuteera, 3rd Stage, Banashankar...	Addhuri Udupi Bhojana	No	No	3.7/5	88	+91 9620009302	Banashankari	Quick Bites	Masala Dosa	South Indian, North Indian	300	[('Rated 4.0', "RATED\n Great food and proper...	[]	Buffet	Banashankari
4	https://www.zomato.com/bangalore/grand-village...	10, 3rd Floor, Lakshmi Associates, Gandhi Baza...	Grand Village	No	No	3.8/5	166	+91 8026612447\r\n+91 9901210005	Basavanagudi	Casual Dining	Panipuri, Gol Gappe	North Indian, Rajasthani	600	[('Rated 4.0', 'RATED\n Very good restaurant ...	[]	Buffet	Banashankari
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
51712	https://www.zomato.com/bangalore/best-brews-fo...	Four Points by Sheraton Bengaluru, 43/3, White...	Best Brews - Four Points by Sheraton Bengaluru...	No	No	3.6 /5	27	080 40301477	Whitefield	Bar	NaN	Continental	1,500	[('Rated 5.0', "RATED\n Food and service are ...	[]	Pubs and bars	Whitefield
51713	https://www.zomato.com/bangalore/vinod-bar-and...	Number 10, Garudachar Palya, Mahadevapura, Whi...	Vinod Bar And Restaurant	No	No	NaN	0	+91 8197675843	Whitefield	Bar	NaN	Finger Food	600	[]	[]	Pubs and bars	Whitefield
51714	https://www.zomato.com/bangalore/plunge-sherat...	Sheraton Grand Bengaluru Whitefield Hotel & Co...	Plunge - Sheraton Grand Bengaluru Whitefield H...	No	No	NaN	0	NaN	Whitefield	Bar	NaN	Finger Food	2,000	[]	[]	Pubs and bars	Whitefield
51715	https://www.zomato.com/bangalore/chime-sherato...	Sheraton Grand Bengaluru Whitefield Hotel & Co...	Chime - Sheraton Grand Bengaluru Whitefield Ho...	No	Yes	4.3 /5	236	080 49652769	ITPL Main Road, Whitefield	Bar	Cocktails, Pizza, Buttermilk	Finger Food	2,500	[('Rated 4.0', 'RATED\n Nice and friendly pla...	[]	Pubs and bars	Whitefield
51716	https://www.zomato.com/bangalore/the-nest-the-...	ITPL Main Road, KIADB Export Promotion Industr...	The Nest - The Den Bengaluru	No	No	3.4 /5	13	+91 8071117272	ITPL Main Road, Whitefield	Bar, Casual Dining	NaN	Finger Food, North Indian, Continental	1,500	[('Rated 5.0', 'RATED\n Great ambience , look...	[]	Pubs and bars	Whitefield

	online_order	book_table	rate	votes	location	rest_type	cuisines	cost	menu_item
0	1	1	4.1	775	1	20	1386	800.0	5047
1	1	0	4.1	787	1	20	594	800.0	5047
2	1	0	3.8	918	1	16	484	800.0	5047
3	0	0	3.7	88	1	62	1587	300.0	5047
4	0	0	3.8	166	4	20	1406	600.0	5047
...	...	...	...	...	...	...	...	...	...
23243	1	1	3.8	128	84	5	1318	1200.0	5047
23244	0	0	3.9	77	84	38	878	2000.0	5047
23245	0	0	2.8	161	84	21	539	1200.0	5047
23246	0	0	2.5	81	84	21	80	800.0	5047
23247	0	1	4.3	236	25	4	878	2500.0	5047